Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining

Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining

We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of...

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Bibliographic Details
Main Authors: Emiliano eTorre, David ePicado-Muino, Michael eDenker, Christian eBorgelt, Sonja eGrün
Format: Article
Language:English
Published: Frontiers Media S.A. 2013-10-01
Series:Frontiers in Computational Neuroscience
Subjects:
Data Mining
higher-order correlations
multiple testing
spike patterns
spike synchrony
neuronal cell assemblies
Online Access:http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00132/full

Internet

http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00132/full

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